Logistics / Supply ChainNewVerified Real deployment at a named or precisely described company, with a measured result from a primary source.

AI freight planning: spot-rate quotes in minutes, consolidated loads and carrier scorecards

Added 2026-10-15 · Last updated 2026-10-15

Role

Logistics & transportation · Typical users: logistics coordinator, transportation planner, shipping supervisor, supply chain manager

The problem

Outbound freight is still run from email and spreadsheets in many plants. Every spot shipment means emailing a few carriers, waiting hours for quotes and picking one with no benchmark. Orders going to nearby customers ship on separate partial trucks because nobody sees them together in time, carrier on-time performance is argued about instead of measured, and proof of delivery comes back on paper days later. At a Fortune 500 engine manufacturer described by its TMS vendor, the ERP handled transactions but not freight optimization, spot freight procurement was entirely manual and took hours per request, and proof of delivery was paper-based across more than 28,000 consignees in 190 countries.

Step by step

1. Export three months of shipments from the ERP or carrier invoices: ship date, origin plant, destination postal code, weight, pallets, mode, carrier, quoted and invoiced cost, promised and actual delivery date.
2. Measure the baseline before any tool: spot quote time per request (ask the coordinator to log it for two weeks), share of partial loads, on-time delivery by carrier, and cost per shipment by lane.
3. Use AI on the export to group open orders by lane and ship window and list consolidation candidates; the planner checks each one against customer due dates, dock hours and load limits.
4. Standardize the spot RFQ: one template with origin, destination, dates, dimensions, weight, accessorials and hazmat status, sent to more carriers at once, with replies collected in one table.
5. Award with a benchmark: compare each quote to what you paid on that lane recently and to a market rate index if you have one; a person approves every award.
6. Score carriers monthly on actual pickup and delivery times, damages and invoice disputes, and use the scorecard in allocation and rate talks.
7. Digitize proof of delivery (carrier app, photo or e-signature) so delivery exceptions and detention claims are visible the same day.
8. If the manual pilot shows real gains, then evaluate a TMS that connects to your ERP, and check its invoice-audit features against your own freight-invoice review.

Tools

• Pando (AI-powered TMS): used at the Fortune 500 engine manufacturer
• Other TMS and freight platforms to evaluate: Oracle Transportation Management, SAP Transportation Management, Manhattan TMS, Blue Yonder TMS, e2open, Uber Freight TMS; for smaller shippers, freight marketplaces with instant quotes
• Any LLM (ChatGPT, Claude, Copilot) with the prompt below, on an export of your open orders and past shipments, to find consolidation candidates and draft a spot RFQ before buying anything

As reported by the source; most figures are vendor or self-reported and not independently verified.

Human review: cautions

• All figures come from the TMS vendor's own customer story about an unnamed company, with no baseline for on-time delivery or cost; measure your own baseline first.
• Consolidation can break customer delivery windows or dock rules: a planner approves every combined load.
• A cheaper spot quote is not always cheaper: check accessorials, detention terms and the carrier's on-time record before awarding.
• Do not paste customer names, prices or contracts into a public chatbot; use an approved enterprise tool or remove the identifiers.
• A TMS does not replace freight-invoice review: surcharges and wrong-party charges still need a check before payment.

You are a transportation planner at a manufacturing plant.

Inputs I will paste: (1) open orders to ship in the next [7] days [order ID, customer, destination city and postal code, requested ship date, latest delivery date, weight, pallets, special requirements]; (2) our lane history for the last 3 months [ship date, destination, carrier, mode (FTL/LTL/parcel), cost paid, promised and actual delivery date]; (3) our constraints [truck capacity in pallets and weight, dock hours, customers that cannot accept early delivery].

Do this:
1. Group the open orders into consolidation candidates by lane and ship window. For each group list the order IDs, the combined weight and pallets copied from my data, and whether every order's delivery window still works. Mark any group you are not sure about 'PLANNER CHECK'.
2. For orders that need a spot quote, draft one RFQ email per lane with origin, destination, ship date, pallets, weight, and special requirements, ready for me to send to carriers.
3. From the lane history, build a table per carrier and lane: number of shipments, number delivered after the promised date, and the costs paid (copy them; do not average or total them), so I can compare quotes.
4. List the data that is missing or inconsistent (no postal code, no weight, delivery date before ship date).

Do not add up weights, compute averages or percentages, or calculate costs yourself: copy the values as given, and I will do the totals in my spreadsheet. Do not award any load or contact any carrier.
Use only the data I paste in; if something is missing, write 'not found' or list it as a question instead of guessing. Do not send, order, post or change anything in any system: produce drafts for my review and approval.

Sources

• https://pando.ai/fortune-500-engine-manufacturer-achieves-94-otif-with-ai-powered-tms